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9th International conference on Wireless and Telematics, ICWT 2023

作者： Rhomadoni, Ali Suryana, Joko School of Electrical Engineering and Informatics Institut Teknologi Bandung Telematics Laboratory Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Radio Telecommunication and Microwave Laboratory Bandung Indonesia

ISBN: (纸本)9798350305029

The ASTERIX organization has introduced a new data format standard, ASTERIX Cat 015, for Independent Non-Coherent Surveillance (INCS) sensor data. This standard is particularly suitable for exchanging Passive Coherent Location (PCL) radar data. In this paper, a practical method for implementing ASTERIX Cat 015 as a data format for PCL radar data exchange, with predefined transmitter and receiver positions was presented. Additionally, a technique for detecting targets using ellipse intersection calculation, which utilizes the ASTERIX Cat 016 data stored in the database for passive radar configuration information was proposed. Finally, The feasibility of using ASTERIX Cat 015 as a standardized data format for passive radar data exchange was demonstrated successfully with data radar obtained from passive radar system developed by the Bandung Institute of technology and the Indonesian Ministry of Defense. © 2023 IEEE.

关键词： Electronic data interchange

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A Systematic Review on sensor Fusion technology in Autonomous Vehicles 4

A Systematic Review on Sensor Fusion Technology in Autonomou...

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4th International conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kumar, Akshay Stephen, K. Sai Sabitha, A. Hindustan Institute of Technology and Science Dept of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

sensor fusion technology is a critical component of autonomous vehicles, enabling them to perceive and respond to their environment with greater accuracy and speed. This technology integrates data from multiple sensors, such as lidar, radar, cameras, and GPS, to create a comprehensive understanding of the vehicle's surroundings. By combining and analyzing this data, sensor fusion technology can identify objects, predict their movements, and make decisions about the best course of action. In this way, it enables autonomous vehicles to operate safely and reliably in complex environments, such as urban streets or highways. sensor fusion technology is a rapidly evolving field, and researchers are continually developing new algorithms and techniques to improve its accuracy and reliability. © 2023 IEEE.

关键词： Autonomous vehicles

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Gamification of RF data Acquisition for Classification of Natural Human Gestures

Gamification of RF Data Acquisition for Classification of Na...

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IEEE radar conference (radarConf)

作者： Kurtoglu, Emre DeHaan, Kenneth Pezzarossi, Caroline Kobek Griffin, Darrin J. Crawford, Chris Gurbuz, Sevgi Z. Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA Gallaudet Univ Dept Amer Sign Language Washington DC USA Gallaudet Univ Dept Psychol Washington DC USA Univ Alabama Dept Commun Studies Tuscaloosa AL USA Univ Alabama Dept Comp Sci Tuscaloosa AL USA

ISBN: (纸本)9798350329216;9798350329209

In recent years, there have been significant developments in radio frequency (RF) sensor technology used in human-computer interaction (HCI) applications, specifically in areas like gesture recognition and more broadly, human activity recognition. Although extensive research has been conducted on these subjects, most experiments involve controlled settings where participants are instructed on how to perform specific movements. However, when such experiments are conducted on sign language recognition they lack capturing dialectal and background-related diversities. In this work, we explore the differences in RF datasets acquired under controlled experimental settings and in free form environments where users were not constrained by the experimental instructions and limitations. We show that directed (i.e., controlled) data acquisition approaches result in over-optimistic performances which do not perform well on naturally acquired data samples in a real-world use case. We evaluate different approaches on generating synthetic samples from directed dataset, but show that such methods do not offer much benefit over collecting natural data. Therefore, we propose an interactive data acquisition paradigm through gamification. We show that the proposed approach enables the recognition of American Sign Language (ASL) in real-world settings by achieving 69% accuracy on 29 words.

关键词： Micro-Doppler spectrogram American Sign Language RF sensors deep neural networks multi-modal

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Autonomous Navigation using Lidar sensor in ROS and GAZEBO 2

Autonomous Navigation using Lidar Sensor in ROS and GAZEBO

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2nd IEEE International conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Subhashini, K. Rathiksha, G. Keerthana, B. Amirthavarshini, P. Sri Sai Ram Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350383287

The field of autonomous navigation has been significantly advanced. Autonomous Vehicle depends on their perception system to obtain critical data pertaining to their immediate environment. This article elucidates the process of developing an autonomous Segway model which uses LIDAR sensor. This Segway can only be used on smooth indoor surfaces and continues on the designated path, dodging obstacles. LiDAR-based autonomous navigation system outlines the development of algorithms for obstacle detection, path planning, and localization. In response to the imperative requirement of safety and precision, LIDAR integration system has been introduced as a supplementary element alongside camera and radar based perception system. Conversely, contemporary practice involves planning most processes through simulations, enabling the anticipation and resolution of future production issues. Therefore, the entire stimulation is done within the Gazebo simulation environment and the entire data communication is managed through ROS (Robot Operating System) which provides a robust platform for implementing in Segway. The robot operating system offers specific configuration options and functionality for a mobile Segway working in autonomous navigation mode. There are phases for making a map, localizing it, and navigating it. The settings of the simulation control parameters as well as the Segway model itself have been optimized and fine-tuned through experiments. © 2024 IEEE.

关键词： Optical radar

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Missing Modality Completion for Multi Frequency radar HRRP Recognition Using GAN 2

Missing Modality Completion for Multi Frequency Radar HRRP R...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Zhou, Qiang Yu, Bingqian Meng, Yuanhao Zhang, Xin Li, Yang Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is one of the commonly used methods in radar automatic target recognition (RATR). Recently, obtaining HRRP under different modalities, such as frequency and polarization, to improve the RATR performance has become an important development direction. However, in practical applications, sensor failures or interference may lead to modality loss during the model testing phase, resulting in decreased performance or even causing the model to fail to function properly. This paper primarily addresses the scenario of frequency modality loss and specifically proposes a modality completion and recognition network with generative adversarial network-embedded (MCRN-GAN) model. The model first generates and completes the missing modality features using the frequency feature transformation loop (FFTL) based on GAN. Subsequently, it fuses multi-modal features based on spatial and channel attention mechanisms to obtain a joint representation of dual-frequency features. Finally, a fully connected layer is used to complete the classification. Experiments on Ku and W frequency modality datasets demonstrates that the proposed method can effectively handle missing modality data after sufficient training without significant performance loss. © 2024 IEEE.

关键词： Frequency modulation

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Design Of Truck Ultra-High Warning System Based On radar Detection technology 5

Design Of Truck Ultra-High Warning System Based On Radar Det...

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5th International conference on Electronic Engineering and Informatics, EEI 2023

作者： Mu, Ruihou Yang, Shifeng Gao, Jie Shi, Hongtong College of Electronic Information and Automation Tianjin University of Science and Technology Tust Tianjin China

ISBN: (纸本)9798350327076

With the rapid development of the transportation industry, the number of truck transportation under railway Bridges and culverts has increased sharply, and some illegal ultra-high vehicles have also increased significantly. In order to ensure the safety and stability of railway operation, an all-round, high-reliability ultra-high warning system for truck is proposed based on image processing, vibration monitoring and radar technology. Firstly, the point cloud data of vehicle within the monitoring range of laser radar is constructed, and the vehicle is analyzed comprehensively according to the data, and the image processing technology is used to identify whether the vehicle is exceeds the height limit. Then, a three-axis acceleration sensor network with low power sleep technology is designed to sense the collision process between the vehicle and the height limit protection frame, which can provide data support for the rapid evaluation of collision accidents and improve the efficiency of accident handling. The final experimental results show that the system can detect whether the vehicle is exceeds the height limit at 25 meters away in real time, effectively solve the problem of vehicle escape and difficult to determine the damage, and has high accuracy and reliability, and has wide application value. © 2023 IEEE.

关键词： sensor networks

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Deep Learning-Based Multisensor Approach for Precision Agricultural Crop Classification Based on Nitrogen Levels

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Reji, J. Nidamanuri, Rama Rao Indian Inst Sci Educ & Res Sch Data Sci Thiruvananthapuram 695551 Kerala India Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram 695547 Kerala India

Accurate classification of crops at the patch level based on nutrient status, particularly nitrogen (N) levels, is essential for advancing precision agriculture (PA). While recent advancements in remote sensing, scalable computing, and visualization technologies have enabled high-resolution plant monitoring, the spectral similarity among crops remains a challenge for precise classification using remote sensing data. This study introduces a multisensor fusion approach, integrating terrestrial LiDAR point cloud data and WorldView-III multispectral imagery within a deep learning (DL) framework to classify cabbage, eggplant, and tomato across different N levels. By combining structural and spectral information, this method effectively captures N-induced growth variations, leading to improved crop discrimination. Our results demonstrate that applying a deep convolutional neural network (DCNN) model to the fused dataset enhances classification accuracy by 13%-16% compared to using multispectral data alone. The incorporation of LiDAR data plays a key role in capturing canopy structure, significantly improving classification performance. Additionally, our DL approach outperforms traditional machine-learning methods, including the random forest (RF) classifier, reinforcing the advantages of DL for N-sensitive crop classification. By leveraging multisensor integration and DL, this study presents a robust and scalable approach for enhancing crop classification accuracy, with significant potential for advancing PA and site-specific nutrient management.

关键词： Crops Accuracy Nitrogen Laser radar Deep learning Tin Point cloud compression Remote sensing Image segmentation Three-dimensional displays Convolutional neural network (CNN) deep learning (DL) LiDAR multisensor fusion multispectral imagery nitrogen (N) levels patch level precision agriculture (PA) terrestrial laser scanning (TLS)

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Visual Analytics of Streaming data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

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11th International conference on Electrical, Electronic and Computing Engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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Research on Monitoring technology Based on the Fusion of 4D Millimeter Wave radar and Machine Vision

Research on Monitoring Technology Based on the Fusion of 4D ...

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International conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Li, Kaishuo Chen, Xiaozhong Xu, Tao Yang, Xiaohui Huang, Ning Li, Mengyang Li, Guangtao Xu, Wenjie University of Jinan Jinan250022 China Shandong Institutes of Industrial Technology Jinan250102 China Tianjin University Tianjin300072 China Jiangxi Solidat Measurement Instrument Co. Ltd. Jiangxi Shangrao330046 China

ISBN: (纸本)9789819708543

Advanced monitoring system requires the sensing module to cover the whole target and the whole working condition, but the traditional monocular camera-based surveillance system can hardly meet this requirement. To address the limitations of a single sensor, a target detection method is designed based on 4D millimeter wave radar and monocular camera fusion. Specifically, we use the DBSCAN algorithm and the Yolov5 target detection algorithm to detect valid targets in radar data and camera data, respectively. Then we construct the coordinate system transformation model and fit the homography matrix using the homography transformation principle and the least-squares estimation method. Finally, a weighted distance-based global nearest neighbor data association algorithm is designed and compared with its method. The detection precision of the proposed association algorithm reaches 81.0%, which is higher than other traditional methods. The experimental results indicate that the detection method is adequate for practical monitoring applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data fusion

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A groundwater stratification monitoring instrument for one shaft with multi-layer well 9

A groundwater stratification monitoring instrument for one s...

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9th International conference on Computer and Communication Systems (ICCCS)

作者： Zhang, Lei Li, Kang Ren, Meijuan Song, Qingguo China Geol Survey Ctr Hydrogeol & Environm Geol Baoding Peoples R China Aimoli Hebei Technol Co LTD Instrument R&D Dept Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350350227;9798350350210

A groundwater layered monitoring system is created for the current situation of monitoring groundwater in a one shaft with multi-layer well. The construction and features of a one shaft with multi-layer well are introduced. It consists of a communication host and several collecting probes. Water level sensors can be connected using standard protocol communication. The automated sensor collection and monitoring data analysis made possible by the integrated recognition algorithm enhances the system's suitability for field deployment. Linear correction and uniform calibration are performed to ensure the accuracy of the water level sensor. Various steps are taken, starting with hardware and software, to increase system stability while simultaneously building a three-dimensional visualization information platform for monitoring groundwater stratification. The system is applied in farmland area, and the working efficiency is greatly improved.

关键词： groundwater stratified monitoring multi-source data acquisition data correction IoT

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